To assess the validity of the regression calibration methodology, we consider plots of the various proxies against one another. There is an approximately linear relationship between the various proxies which suggests the use of a linear calibration function is appropriate. This is further emphasized in the discussion by [1]}, where it is noted that the transformed blood pressure covariates appear approximately normal. Considering the diagnostic plots for the SIMEX corrections shows that the impact of measurement error on the estimates is most pronounced in the blood pressure slope coefficient. The other slope parameter estimates appear approximately constant over the various values for \(\lambda \) , and as a result, taking a linear extrapolant seems justified. For the blood pressure estimates, the non linear extrapolant is used. This follows the general advice for SIMEX in logistic regression [2]}.
